How Agentic AI Will Turn Your Life And Workplace Upside-down

Your Company Needs Small Language Models (SLMs)

So, what exactly are small language models? They are simply language models trained only on specific types of data, that produce customized outputs. A critical advantage of this is the data is kept within the firewall domain, so external SLMs are not being trained on potentially sensitive data. The beauty of SLMs is that they scale both computing and energy use to the project’s actual needs, which can help lower ongoing expenses and reduce environmental impacts.

Another important alternative—domain-specific LLMs—specialize in one type of knowledge rather than offering broader knowledge. Domain-specific LLMs are heavily trained to deeply understand a given category and respond more accurately to queries, by for example a CMO vs. a CFO, in that domain.

AI Agents (Suggesting) Vs. Agentic AI (Acting)

The phrase agentic AI has received a lot of attention from technologists, analysts, and enterprises, leaving some to wonder what all the excitement is about. Discerning human agents from AI agents and agentic AI can understandably be confusing. The latter term has its roots in psychology. Agentic denotes the concept of agency, or the sense of control and the ability to handle tasks and situations.

A recent NY Times article attributed the agentic AI term’s origins to AI researcher Andrew Ng. It describes AI systems that exhibit agency. This means AI that can autonomously pursue goals, make decisions, and dynamically adapt to changing conditions without human intervention. These systems operate with a higher level of independence than traditional AI, often exhibiting capabilities like goal setting, prioritization, and collaboration.

Agentic AI differs from simpler AI agents because it focuses on independence, self-directed action, and broader functionality in handling complex tasks and environments. You could say, it can do things without humans. AI agents, on the other hand, have been around for decades. The rise of machine and deep learning in the 2010s introduced cognitive intelligence. Generative AI (like GPT models) in the 2020s added sophisticated natural language understanding and reasoning, creating a through line from traditional AI agents to agentic AI.

If you are wondering whether you should pay attention to this one: Gartner estimates that by 2028, 33% of enterprises will include agentic AI, up from less than 1% in 2024. That’s a yes!

Some may wonder what this next generation of AI tech really means in terms of applications. What kind of problems can it solve, and how can it elevate AI’s role in transforming the workplace, safely, with all the guardrails everyone is talking about?

At its core, much of this advancement boils down to improved information access, actionable insights and task automation. Despite technological progress, employees still spend too much time and effort searching for information in siloed systems, navigating multiple applications, and managing routine tasks.

Anyone who’s worked for an enterprise can relate—I know I can. Common frustrations include searching for company documents buried in Slack, SharePoint or Outlook, or tracking down customer information spread across multiple systems. Workers often must juggle countless applications, spending hours each week on tasks that could be automated. According to Forrester, knowledge workers spend 30% of their time looking for information and 80% believe reducing silos is a top priority.

At first glance, these challenges resemble those that digital transformation, workflow and automation technologies have aimed to solve for years. Sure, AI should be able to help and is progressing by leaps and bounds. But solving these kinds of problems transcends the typical GenAI chat and content creation use cases that everyone is familiar with by now. Large language model prompt-based applications and copilot tools can’t handle complexity—they hallucinate or remain siloed in individual applications.

As the future of work becomes increasingly driven by AI, enterprises need secure and scalable AI tools that empower teams with out-of-the-box capabilities to work more effectively, streamline processes, and significantly reduce time spent on routine tasks.

Enterprises Need AI Concierge Brain Trusts

Another technology is Kore.ai’s AI for Work, just announced today. It is a good example of how cutting-edge technology can reshape our workplaces. The NVIDIA-backed Gartner Magic Quadrant company is accelerating AI adoption and delivering results with a single, secure platform that combines intelligent enterprise search, workflow automation, and multi-agent orchestration.

The company’s roots lie in enterprise tech., and Kore has developing GenAI integration over the past couple of years. With AI for Work, they are apparently unleashing a conversational, generative, and agentic AI triple threat.

I spoke with CEO and Founder Raj Koneru, who explained further: “This isn’t just another tool – it’s a fundamental transformation in how enterprises harness AI to make work more efficient, intelligent, and valuable across all levels of the organization. Early customer beta results show 30-50% faster information retrieval, workflow automation, and higher productivity overall.”

The no-code AI platform includes prebuilt AI agents and templates for common business workflows and over 100+ integrations to enterprise systems. Its universal orchestrator manages interactions across diverse AI agents, ensuring seamless, secure, and context-aware task execution.

C-Suite Leaders Reaping AI Benefits

AI for Work enables decision-making for processes, which will be a boon for those struggling with limited data or document analysis resources. Kore’s AI agents can scale enterprises’ capabilities, and help teams boost productivity—which will hopefully lead to smarter decisions.

For example, it could transform the access to and synthesis of information from numerous sources through intelligent information handling. Instead of toggling between multiple systems, a salesperson could simply request “Show me ABC Corp’s support history” to get a 360-degree view spanning CRM info, help desk data, email, meeting summaries and financial accounts status—all from one place.

AI for Work can also streamline complex business processes that require multiple handoffs and system interactions through intelligent workflow automation. It’s designed to coordinate complex workflows across specialized AI agents that can search, reason, summarize, generate content, and directly integrate with APIs to seamlessly deliver multistep process execution. It may sound complicated, but typical use cases can be real simple: employee/customer onboarding or IT service management.

When I asked Koneru for a live example, he referenced Yuliya Teteryuk, Customer Care Director at Autodoc, a German online auto parts retailer, who said: “Generative AI is shaking up every aspect of work. That’s why we partnered to integrate AI into our customer and employee support operations. We have observed 74% first-call resolution and significant savings. Our people are happier. We are excited about the simplicity, potential and benefits AI for Work brings to the table.” I like the sound of happier people.

Booming Field Of AI Utopian Options

In addition to LLMs and SLMs which I covered last week, providers are jumping into the agentic AI fray big time. For example, Salesforce announced Agentforce in September and continues its expansion. According to the company’s release, it’s “a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction.” That’s a mouthful.

Just last month, Microsoft’s blog shared news about its autonomous agents that “scale your team like never before,” via agentic tech that advances Microsoft 365 Copilot, making it possible to create autonomous agents with Copilot Studio.

Salesforce’s Benioff emphasizes the importance of collaborative approaches as the world moves to an agentic utopia in his TIME essay: “Harnessing the power of agentic AI effectively will require a multistakeholder approach – businesses, governments, nonprofits, and academia working together to create guardrails and guidelines. The benefits… [for] individuals and businesses will far outweigh the initial disruptions.” Agreed.

What C-Suite Leaders Should Do (Trust) Next

Moving forward, business adoption of AI won’t be one-size-fits-all: Every business will focus on efficiency, selecting the best and least expensive tool to get the job done properly. That means picking the right-sized model for each project, whether a general purpose LLM or smaller and domain-specific LLMs as businesses determine they will deliver better results, require fewer resources, and reduce the need for data to migrate to the cloud.

Given the current state of public confidence in AI-generated answers, it’s clear trusted AI and data will be mandatory for the next wave of business solutions. “When you think about training AI models,” said McMillan, “they must be built on the foundation of great data. That’s what we’re all about, providing that trusted data set and then providing the capabilities and analytics capabilities so clients, and their customers, can trust the outputs.”

In a world that needs higher accuracy and efficiency more than ever before, smaller and domain-specific LLMs offer another option for delivering results companies and the broader public can rely upon. Leaders who continue to invest in their learning journeys will be able to accelerate their company’s AI optimization and become more competitive in their specific market sectors. Enjoy the journey.

Hyper Competitive AI Era Ahead

The opportunities ahead are becoming clearer to some C-suite leaders—larger than any single technology has previously enabled. Yes, agentic AI brings exciting new capabilities that promise to streamline and automate tasks, but its true power lies in integration with GenAI for content creation and actionable insights. Together, these advanced AI platforms democratize technological innovation, enabling employees to leverage intelligent tools without extensive technical expertise. By delivering immediate value across departments, these solutions should facilitate strategic technological transformation.

We are entering a new era of workplace optimization that directly addresses critical enterprise challenges. The ultimate goal is to create a responsive, efficient organizational ecosystem where technology enhances human potential and helps to gain a competitive edge. The C-suite has never been so challenged to stay ahead of their competition through enabling technology. Buckle up, we are in the CaaS Era—Competitiveness-as-a-Service.